The impact of high dimensionality on SVM when classifying ERP data - A solution from LDA

Abstract

Brain-computer interfaces (BCI) based on P300 event-related potentials (ERP) could help to select characters from a visually presented character-matrix. They provide a communication channel for users with neurodegenerative disease. Associated to these kinds of BCI systems, there is the problem to determine whether or not a P300 was actually produced in response to the stimuli. The design of this classification step involves the choice of one or several classification algorithms from many alternatives. Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA) have been used to achieve acceptable results in numerous P300 BCI applications. However, both of them suffers from the high dimensional problem which leads to deterioration of their performance. In this paper, we introduce a novel and combined approach of LDA and SVM to reduce the negative effect of high dimensional data on SVM and LDA, and investigate the performance of our method. The results shows that the new approach achieves similar or slightly better performance than the state-of-art method.